




'''simple docstring'''


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class  __lowercase    (	__magic_name__			):
   _a    						=   (DDIMParallelScheduler,)
   _a    						=   (("""eta""", 0.0), ("""num_inference_steps""", 50))







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     int:
     __a								=       {
         'num_train_timesteps': 1000,
         'beta_start': 0.0_001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'clip_sample': True,
     }

     config.update(**UpperCamelCase   )
     return config







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     List[str]:
     __a								=       self.scheduler_classes[0]
     __a								=       self.get_scheduler_config(**UpperCamelCase   )
     __a								=       scheduler_class(**UpperCamelCase   )

     __a						,							__a								=       10, 0.0

     __a								=       self.dummy_model()
     __a								=       self.dummy_sample_deter

     scheduler.set_timesteps(UpperCamelCase   )

     for t in scheduler.timesteps:
       __a								=       model(UpperCamelCase   ,				UpperCamelCase   )
       __a								=       scheduler.step(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ).prev_sample

     return sample







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     for timesteps in [100, 500, 1000]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=UpperCamelCase   )

     __a								=       self.scheduler_classes[0]
     __a								=       self.get_scheduler_config(steps_offset=1   )
     __a								=       scheduler_class(**UpperCamelCase   )
     scheduler.set_timesteps(5   )
     assert torch.equal(scheduler.timesteps   ,				torch.LongTensor([801, 601, 401, 201, 1]   )   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]   ,				[0.002, 0.02, 0.2, 2]   ):
       self.check_over_configs(beta_start=UpperCamelCase   ,				beta_end=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     for timestep_spacing in ["trailing", "leading"]:
       self.check_over_configs(timestep_spacing=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:
     for rescale_betas_zero_snr in [True, False]:
       self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:
     self.check_over_configs(thresholding=UpperCamelCase   )
     for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(
             thresholding=UpperCamelCase   ,				prediction_type=UpperCamelCase   ,				sample_max_value=UpperCamelCase   ,				)







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     for t in [1, 10, 49]:
       self.check_over_forward(time_step=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     for t, num_inference_steps in zip([1, 10, 50]   ,				[10, 50, 500]   ):
       self.check_over_forward(time_step=UpperCamelCase   ,				num_inference_steps=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     for t, eta in zip([1, 10, 49]   ,				[0.0, 0.5, 1.0]   ):
       self.check_over_forward(time_step=UpperCamelCase   ,				eta=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       self.scheduler_classes[0]
     __a								=       self.get_scheduler_config()
     __a								=       scheduler_class(**UpperCamelCase   )

     assert torch.sum(torch.abs(scheduler._get_variance(0   ,				0   ) - 0.0   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(420   ,				400   ) - 0.14_771   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(980   ,				960   ) - 0.32_460   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(0   ,				0   ) - 0.0   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(487   ,				486   ) - 0.00_979   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999   ,				998   ) - 0.02   )   ) < 1e-5







   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       self.scheduler_classes[0]
     __a								=       self.get_scheduler_config()
     __a								=       scheduler_class(**UpperCamelCase   )

     __a						,							__a								=       10, 0.0
     scheduler.set_timesteps(UpperCamelCase   )

     __a								=       self.dummy_model()
     __a								=       self.dummy_sample_deter
     __a								=       self.dummy_sample_deter + 0.1
     __a								=       self.dummy_sample_deter - 0.1

     __a								=       samplea.shape[0]
     __a								=       torch.stack([samplea, samplea, samplea]   ,				dim=0   )
     __a								=       torch.arange(UpperCamelCase   )[0:3, None].repeat(1   ,				UpperCamelCase   )

     __a								=       model(samples.flatten(0   ,				1   )   ,				timesteps.flatten(0   ,				1   )   )
     __a								=       scheduler.batch_step_no_noise(UpperCamelCase   ,				timesteps.flatten(0   ,				1   )   ,				samples.flatten(0   ,				1   )   ,				UpperCamelCase   )

     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     assert abs(result_sum.item() - 1_147.7_904   ) < 1e-2
     assert abs(result_mean.item() - 0.4_982   ) < 1e-3







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       self.full_loop()

     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     assert abs(result_sum.item() - 172.0_067   ) < 1e-2
     assert abs(result_mean.item() - 0.223_967   ) < 1e-3







   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       self.full_loop(prediction_type='v_prediction'   )

     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     assert abs(result_sum.item() - 52.5_302   ) < 1e-2
     assert abs(result_mean.item() - 0.0_684   ) < 1e-3







   def 		UpperCamelCase__      (						self   )					->     Dict:
     # We specify different beta, so that the first alpha is 0.99
     __a								=       self.full_loop(set_alpha_to_one=UpperCamelCase   ,				beta_start=0.01   )
     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     assert abs(result_sum.item() - 149.8_295   ) < 1e-2
     assert abs(result_mean.item() - 0.1_951   ) < 1e-3







   def 		UpperCamelCase__      (						self   )					->     int:
     # We specify different beta, so that the first alpha is 0.99
     __a								=       self.full_loop(set_alpha_to_one=UpperCamelCase   ,				beta_start=0.01   )
     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     assert abs(result_sum.item() - 149.0_784   ) < 1e-2
     assert abs(result_mean.item() - 0.1_941   ) < 1e-3









'''simple docstring'''


from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
UpperCAmelCase_					=   Lock()


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Tuple					,       a_						:				Dict					,       a_						:				Optional[Any]					,       a_						:				str					,       a_						:				Any					,       a_						:				Optional[Any]      ):
  global process_lock

  # we perform n swaps since after n swaps we know we are sorted
  # we *could* stop early if we are sorted already, but it takes as long to
  # find out we are sorted as it does to sort the list with this algorithm
  for i in range(0					,       10      ):
    if (i + position) % 2 == 0 and r_send is not None:
      # send your value to your right neighbor
      process_lock.acquire()
      r_send[1].send(a_      )
      process_lock.release()

      # receive your right neighbor's value
      process_lock.acquire()
      __a								=       rr_cv[0].recv()
      process_lock.release()

      # take the lower value since you are on the left
      __a								=       min(a_					,       a_      )
    elif (i + position) % 2 != 0 and l_send is not None:
      # send your value to your left neighbor
      process_lock.acquire()
      l_send[1].send(a_      )
      process_lock.release()

      # receive your left neighbor's value
      process_lock.acquire()
      __a								=       lr_cv[0].recv()
      process_lock.release()

      # take the higher value since you are on the right
      __a								=       max(a_					,       a_      )
    # after all swaps are performed, send the values back to main
  result_pipe[1].send(a_      )


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]      ):
  __a								=       []
  __a								=       []
  # initialize the list of pipes where the values will be retrieved
  for _ in arr:
    result_pipe.append(Pipe()      )
  # creates the processes
  # the first and last process only have one neighbor so they are made outside
  # of the loop
  __a								=       Pipe()
  __a								=       Pipe()
  process_array_.append(
      Process(
          target=a_					,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])					,       )      )
  __a								=       temp_rs
  __a								=       temp_rr

  for i in range(1					,       len(a_      ) - 1      ):
    __a								=       Pipe()
    __a								=       Pipe()
    process_array_.append(
        Process(
            target=a_					,       args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])					,       )      )
    __a								=       temp_rs
    __a								=       temp_rr

  process_array_.append(
      Process(
          target=a_					,       args=(
              len(a_      ) - 1,
              arr[len(a_      ) - 1],
              temp_ls,
              None,
              temp_lr,
              None,
              result_pipe[len(a_      ) - 1],
          )					,       )      )

  # start the processes
  for p in process_array_:
    p.start()

  # wait for the processes to end and write their values to the list
  for p in range(0					,       len(a_      )      ):
    __a								=       result_pipe[p][0].recv()
    process_array_[p].join()
  return arr


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       list(range(10					,       0					,       -1      )      )
  print('Initial List'      )
  print(*a_      )
  __a								=       odd_even_transposition(a_      )
  print('Sorted List\n'      )
  print(*a_      )


if __name__ == "__main__":
  main()



